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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

Showing 771780 of 9051 papers

TitleStatusHype
Diversity By Design: Leveraging Distribution Matching for Offline Model-Based OptimizationCode0
Diverse Preference OptimizationCode2
Genetic Algorithm with Innovative Chromosome Patterns in the Breeding ProcessCode0
Differentiable Projection-based Learn to Optimize in Wireless Network-Part I: Convex Constrained (Non-)Convex Programming0
Accelerated DC loadflow solver for topology optimization0
Dynamics of Transient Structure in In-Context Linear Regression Transformers0
General Scene Adaptation for Vision-and-Language NavigationCode2
DFPE: A Diverse Fingerprint Ensemble for Enhancing LLM PerformanceCode0
Human-Aligned Skill Discovery: Balancing Behaviour Exploration and Alignment0
RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution SamplesCode0
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